Finally, we used \(ME\)  to infer a biomass distribution \(\mathcal {P_{ME}}\)  for each experimental condition.
In order to do that, and in analogy with the simple model above, we computed the two free components on the \({\beta }\)  vector that allow us to enforce the moment constraints (\(\bar{z}= D\)  and \(\bar{u_g}\le c_gD / X\) ) and maximize the entropy (see Appendix  (REF )).
Due to the large number of variables involved, in this case, the \(\mathcal {P_{ME}}\)  functional becomes intractable, and so, we use \(Expectation\ Propagation\)  [1]} in order to approximate these distributions (see Appendix (REF ) for details).
Additionally, we used a set of four \(FBA\)  formulations as reference to compare the performance of \(ME\) .
Since for a genome-scale network \(\bar{\mathbb {V}}\)  is not as simple as in the toy model, the full set of possible lineal \(FBA\)  solutions becomes intractable.
We first introduced two objective functions common in the literature.
It has been found that for chemostat cultures, the maximization of \(atp\)  or biomass yield (equivalent to the minimization of \(\bar{u_g}\) ) objectives provide better results approximating experimental data than other tested functions [2]}.
In addition to those two, we defined the maximization of the glucose uptake \(\bar{u_g}\)  (motivated by the glucose-limited condition) and the traditional maximization of biomass rate as objective functions to be tested.
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